The artificial intelligence cooperative: READ-COOP, Transkribus, and the benefits of shared community infrastructure for automated text recognition

Background

Artificial Intelligence (AI) and Machine Learning (ML) technologies are integral to developing sophisticated digital infrastructures. Ownership and stakeholder input are critical for creating AI systems that are both innovative and accountable. This paper examines READ-COOP (https://readcoop.eu), the first platform cooperative to develop and host its own AI and ML tools (https://transkribus.org). This case study demonstrates an alternative cooperative governance model for responsible AI infrastructure.

Methods

We employ Research In Action and qualitative questionnaires to analyse the development of READ-COOP, following its transition from funded European Commission (EC) project to cooperative business. We assess the cooperative’s structure, management, and community engagement from 2019 to 2024. Data was collected on membership dynamics, use of Transkribus, and feedback on the cooperative’s governance and operational efficacy.

Results

As of October 2024, READ-COOP has 227 members from 30 countries, fostering a strong user base of over 235,000 registered individuals. Transkribus has transcribed approximately 90 million digital images of historical texts, demonstrating effective AI utilization in the cultural heritage sector, winning the European Union’s Horizon Impact Award 2020. The cooperative approach facilitates democratic decision-making, leading to sustainable growth, and significant stakeholder involvement. Qualitative feedback indicates high levels of satisfaction with the cooperative’s governance and the perceived integrity and utility of the AI infrastructure.

Conclusions

READ-COOP exemplifies that a cooperative business model can effectively sustain AI and ML infrastructures while promoting democratic participation and equitable ownership. This offers a viable blueprint for other sectors seeking to develop responsible and trustworthy AI solutions. We suggest that cooperative frameworks are particularly suitable for AI infrastructures initially funded through public grants, providing a sustainable transition from public development to long-term, sustainable community-ownership. We recommend wider application and exploration of cooperative models for hosting and developing AI and ML technologies to ensure their responsible creation, governance, and use.